Passenger density and flow analysis and city zones and bus stops classification for public bus service management

• Raul S. Barth Federal University of Rio Grande do Sul
• Renata Galante Federal University of Rio Grande do Sul

DOI: https://doi.org/10.5753/sbbd.2016.24331

Abstract

This work presents, for the first time in literature, a low-cost framework to mine data obtained from passengers smart cards, buses GPS and bus stops geolocation using Lambda Architecture approach. Operators, companies, government and passengers will use this knowledge for improving usability, comfort, and quality of transportation service. This analysis gives greater insight into the volume and flow of passengers and the real existing demand for bus services, facilitating its control and management, allowing decision-making. As result, bus stops and city areas are classified according to buses demand.

Keywords: Data Mining, Bus Service Management, Lambda Architecture

References

United Nations (2011). “Population Distribution, urbanization, internal migration and development: An international perspective”

IPEA (2015) - Instituto de Pesquisa Econômica Aplicada.

Pellicer, S. et al. (2013) “A Global Perspective of Smart Cities: A Survey”.

Qing, Z. et al. (2009) “Public Transport IC Card Data Analysis and Operation Strategy Research Based on Data ining Technology”

Li, Man et al. (2012) “Public Transport Smart Card Data Analysis and Passenger Flow Distribution”

Duan, W. et al. (2012) “Analysis of Single-line Passenger Flow Based on IC Data and GPS Data”

Zhang, J. et al. (2014) “Analysing Passenger Density for Public Bus: Inference of Crowdedness and Evaluation of Scheduling Choices”

Kieu, Le Minh; Bhaskar, Ashish; Chung, Edward (2015) “Passenger Segmentation Using Smart card Data”

Kiran, Mariam et al. (2015) “Lambda architecture for cost-effective batch and speed big data processing”

Shenzhen, in China “Sample Data Description of mPat - http://cloud.siat.ac.cn/mpat/”